By: Bart Baesens, Seppe vanden Broucke
This QA first appeared in Data Science Briefings, the DataMiningApps newsletter as a “Free Tweet Consulting Experience” — where we answer a data science or analytics question of 140 characters maximum. Also want to submit your question? Just Tweet us @DataMiningApps. Want to remain anonymous? Then send us a direct message and we’ll keep all your details private. Subscribe now for free if you want to be the first to receive our articles and stay up to data on data science news, or follow us @DataMiningApps.
You asked: I am hearing a lot about HR analytics recently. Can you give some example applications?
Our answer:
Sure — job recruitment or finding good talent is a challenging endeavor where HR analytics can play a crucial role. More specifically, building analytical based recommender systems allows to target the right jobs to the right people. Ideally, these systems should combine both content and search behavior. The former matches job descriptions to applicant profiles, whereas the latter enriches the applicant’s profile by modelling his/her job searches. Combining both sources of information into an analytical model will allow to build high-performing job recommender systems, which will aid firms in their quest for good talent.
HR Analytics can also improve employee attrition by carefully analyzing historical data about employee churn behavior and relating this to employee and/or job characteristics. This will allow to get insight into why employees churn and which ones are likely to churn in the near future. The output of these analytical models can then be fed to HR retention campaigns, aimed at retaining your key asset: your employees!
The impact of compensation on job motivation and/or satisfaction has been a subject of much debate. Using HR analytics, it becomes possible to relate both using dependency modelling. The resulting insights can then be used to intelligently optimize your remuneration policy so as to keep your workforce optimally engaged.
